Click to Execute and Retrieve Excel Data

This workflow automates the identification of building items, utilizing visual models to analyze item attributes, and combines reverse image search with web scraping to obtain detailed information. Ultimately, the enriched data is automatically updated in the database, significantly improving the accuracy of item recognition and the completeness of the data, while reducing the workload of manual data entry. It is suitable for scenarios such as building surveys, asset management, and product information collection, helping enterprises achieve efficient digital transformation.

This workflow automatically receives images sent by users via a Telegram bot and uploads them to AWS S3 storage. Subsequently, it utilizes AWS Textract for intelligent text recognition, and the extracted text data is automatically written into an Airtable spreadsheet. The entire process achieves full-link automation from image reception and storage to recognition and data entry, effectively reducing manual operations and errors, while improving the speed and accuracy of data processing. It is suitable for various scenarios that require quick extraction and management of text from images.

This workflow automatically scrapes the headlines from Hacker News over the years, organizes key news titles from the same date, and utilizes a large language model for intelligent classification and analysis. It ultimately generates a structured Markdown format insight report, which is pushed to users in real-time via a Telegram channel. This process efficiently addresses the repetitive task of manually organizing news, enhancing the efficiency and timeliness of information retrieval, and is suitable for various scenarios such as technology research, news review, and data analysis.

This workflow can automatically extract images from PDF files and utilize AI models for in-depth analysis of their content. By integrating cloud storage and file processing capabilities, it achieves efficient image recognition and analysis without the need for manual intervention. It is suitable for professionals such as researchers, businesses, and content creators who need to quickly process image information, significantly enhancing data processing efficiency and avoiding repetitive work and information loss. The final analysis results will be compiled into an easily viewable text file for convenient archiving and future use.

This workflow focuses on real-time monitoring of bank statements in a local folder, automatically processing changes such as additions, deletions, and modifications of files, and synchronizing the data to a vector database. It generates text vectors using the Mistral AI model to build an intelligent question-and-answer system, allowing users to efficiently and accurately query historical statement content. This solution significantly enhances the management efficiency of bank statements and the query experience, making it suitable for scenarios such as finance departments, bank customer service, and personal financial analysis.

This workflow is an intelligent data analysis assistant that integrates advanced AI language models with Google Sheets, allowing users to perform data queries and analysis through natural language. Users can easily ask questions, and the AI agent automatically filters, calculates, and aggregates data, returning structured analysis results. The system simplifies complex date and status filtering, making it suitable for scenarios such as e-commerce, finance, and customer service, helping non-technical users quickly extract business insights and improve work efficiency.

This workflow builds an efficient Qdrant MCP server capable of flexibly handling customer review data. It supports insertion, searching, and comparison functions of a vector database, while also integrating advanced APIs such as grouped search and personalized recommendations. By utilizing OpenAI's text embedding technology, the workflow achieves intelligent vectorization of text, enhancing the accuracy of search and recommendations. It is suitable for various scenarios, including customer review analysis, market competition comparison, and personalized recommendations.

This workflow integrates AI intelligent dialogue with Google Sheets data access, allowing users to quickly query customer information using natural language, thereby enhancing data retrieval efficiency. It intelligently interprets user questions and automatically invokes the corresponding tools to obtain the required data, avoiding the cumbersome traditional manual search process. It is suitable for scenarios such as customer service, sales, and data analysis, helping users easily access and analyze information in Google Sheets, thereby improving work efficiency and the value of data utilization.